AI Driver Distraction & Drowsiness Detection with Python&CV

Driver Distraction and Drowsiness Detection System using Python, AI, and Computer Vision

What you’ll learn
Understand the importance of driver drowsiness detection and the impact of distractions on road safety, and how AI-powered systems help mitigate these risks.
Set up a Python development environment and install libraries like OpenCV and MediaPipe for computer vision and distraction detection tasks.
Capture real-time video from a webcam and explore the State Farm Driver Distraction dataset to analyze and classify unsafe driver behaviors.
Extract facial landmarks such as eyes and mouth, and apply ResNet50 to classify ten types of driver distractions with high precision and accuracy.
Calculate Eye Aspect Ratio (EAR) and Mouth Aspect Ratio (MAR) to detect drowsiness, and use visualization to improve deep learning model accuracy.
Implement algorithms to detect fatigue like eye closure and yawning, and optimize model performance using transfer learning and fine-tuning.
Develop a Tkinter-based GUI for real-time drowsiness alerts and distraction detection using live camera feeds with clear visual indicators.
Build an interactive user interface and integrate a web-based dashboard to enhance system usability and remote monitoring capabilities.
Combine all components into a working driver monitoring system that addresses challenges like low-light, occlusions, and varying driver postures.
Troubleshoot real-world issues and deploy the system for practical use in fleet monitoring, AI safety assistance, and driver training programs.

Requirements
Basic understanding of Python programming (helpful but not mandatory).
A laptop or desktop computer with internet access[Windows OS with Minimum 4GB of RAM).
No prior knowledge of AI or Machine Learning is required—this course is beginner-friendly
Enthusiasm to learn and build practical projects using AI and IoT tools.

Description
AI-Powered Driver Monitoring System: Distraction and Drowsiness Detection using Python & Computer Vision Welcome to this all-in-one, hands-on course where you’ll learn to develop an intelligent AI-powered system capable of detecting driver distractions and drowsiness in real-time using Python, Computer Vision, and Deep Learning.This course combines the power of ResNet50 for distraction detection and facial landmark-based algorithms for drowsiness detection, offering a complete solution for road safety and driver monitoring.What You’ll Learn:Distraction Detection Module:Use the State Farm Driver Distraction dataset to train a model that identifies 10 different distraction activities such as texting, eating, adjusting the radio, or talking to passengers.Train a ResNet50 deep learning model using TensorFlow/Keras.Apply data preprocessing, augmentation, transfer learning, and hyperparameter tuning to improve model accuracy.Build a real-time distraction detection system using OpenCV and integrate it with a Tkinter-based GUI and web interface.Deploy your model for use in real-world scenarios like fleet management and AI safety systems.Drowsiness Detection Module:Capture and process real-time video feeds using Python and OpenCV.Extract facial landmarks using MediaPipe to analyze eye and mouth movements.Calculate Eye Aspect Ratio (EAR) and Mouth Aspect Ratio (MAR) to detect signs of fatigue, yawning, and drowsiness.Implement logic to trigger real-time alerts and visual warnings when drowsiness is detected.Create a Tkinter-based UI to display status and metrics in real-time.By the end of this course, you will:Build a dual-function Driver Monitoring System that detects both distractions and drowsiness.Gain practical, hands-on experience in AI, computer vision, deep learning, and GUI development.Be equipped to deploy your project in real-world applications across transportation, logistics, and safety systems.Whether you’re a beginner or an intermediate Python developer, this course is designed to provide valuable, real-world experience in building AI-powered safety solutions.

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